# Learn The Whole AI Pipeline With

You will the learn the suite of tools to build an end-to-end deep learning pipeline.

=== "Tools You Will Learn To Use"
    - PyTorch (CPU/GPU)
    - Scikit-learn (CPU)
    - RAPIDS cuML (GPU)
    - Gym (CPU)
    - Python (scripting)
    - C++ (programming)
    - Bash (scripting)
    - NumPy (CPU)
    - CuPy (GPU)
    - Pandas (CPU)
    - RAPIDS cuDF (GPU)
	- Matplotlib (Plot)
	- Plotly (Plot)
	- Streamlit (Dashboard)
	- CassandraDB (CPU DB)
	- BlazingSQL (GPU DB)

We deploy a top-down approach that enables you to grasp deep learning and deep reinforcement learning theories and code easily and quickly. We have open-sourced all our materials through our [Deep Learning Wizard Tutorials](https://www.deeplearningwizard.com/deep_learning/course_progression/).

To this date, we have taught thousands across more than 120+ countries from students in high school to postgraduates and professionals in leading MNCs and research institutions around the world.

![](/assets/mario_flappy_pacman_optimized.gif)

[^1]: Simulation of deep reinforcement learning agent mastering games like [Super Mario Bros](https://github.com/ppaquette/gym-super-mario), [Flappy Bird](https://github.com/sourabhv/FlapPyBird) and [PacMan](http://ai.berkeley.edu/project_overview.html). These games have APIs for algorithms to interact with the environment, and they are created by talented people so feel free to check out their respective repositories with the links given.